The PE AI adoption benchmark

Most firms are stuck
in the expensive middle

The distribution of AI maturity reflects a market in genuine transition, where the distance between the leaders and the middle is growing faster than the middle is moving.

Finance functions are just automating the back-office

The more important question may be about what is happening inside portfolio company finance functions. When Operating Partners looked honestly at their portfolios, the picture was sobering: only 14% have crossed the threshold from back-office automation into genuine decision intelligence. The remaining 86% are using AI to do existing work faster, not to do fundamentally different, more valuable work.

The exit story for AI-enabled finance is built on better forecasting, more defensible scenario modeling, and finance functions that give acquirers confidence in forward-looking projections. Those capabilities sit almost entirely in the 14% that have moved beyond point-solution automation. The rest are spending AI budget, leaving the most valuable territory untouched, and the most compelling exit narrative unwritten.

The bottleneck isn’t the model, and it never was

Operating Partners were asked to rank the barriers preventing portfolio CFOs from scaling AI. Data infrastructure was the clear frontrunner: poor data quality, fragmented systems, and no clean integrated layer. Talent and capability ranked second, followed by legacy technology stacks. The pattern is telling; the biggest roadblocks to adoption are structural constraints, not tech tools. And they require deliberate investment, sequencing, and time, all things in short supply in a typical PE hold period.

The ranking also reveals something important about where the actual constraint sits. Budget and ROI clarity ranked fifth. GP alignment ranked last. So, this isn’t a resource or mandate issue; sponsors are willing to fund AI investment, and they believe in the direction. What is blocking them is foundational: the data layer, the talent, the systems. Get those right and the rest of the barriers become manageable.

Leave them unaddressed and no amount of tech investment or Operating Partner bandwidth moves the needle.

The deficit starts at close

Understanding why data infrastructure ranks first starts with what firms are actually inheriting at acquisition. The data readiness picture is stark at close: 75% of portfolio companies arrive either unprepared or only partially ready for AI deployment.

Only 9% of sponsors have a real playbook

Fewer than one in ten PE firms has a fully operational AI Center of Excellence providing consistent, structured support across portfolio companies. The majority are operating on informal guidance: sharing tool recommendations and best practices without the governance rigor or accountability that durable capability requires. That approach is serviceable for early-stage experimentation, but it does not scale. It doesn’t produce the kind of documented, repeatable AI operating model that sophisticated buyers will want to see when they run diligence on a portfolio company exit.

There’s a widening mandate gap, but it’s closable

On a 1-to-5 scale measuring how far portfolio CFOs have shifted from backward-looking reporting to forward-looking strategic intelligence, Operating Partners rated their portfolios 2.3 out of 5. That score reflects a structural problem: most finance functions were built to report on the past. Retrofitting them for forward-looking intelligence while the quarter is closing is a different kind of hard than most transformation initiatives. The data ahead shows where the path clears.

Part of what drives that score is the data infrastructure problem. You cannot build an AI-enabled FP&A function on fragmented ERP data. CFOs’ posture makes more sense read against that backdrop: 52% fall into evaluative or early-stage categories, often because they are waiting for the conditions that make proactive adoption viable (cleaner data, clearer direction, a partner who can help them move fast without breaking things). Only 17% have fully claimed AI transformation as a strategic mandate with dedicated budget and board visibility.

That 17% is where the case studies are written.

The value is in the forecast

When Operating Partners ranked the highest near-term AI opportunities for portfolio CFOs, FP&A and scenario modeling ranked first by a significant margin. Close cycle acceleration ranked second, with margin intelligence in third. What connects these priorities is a fundamental shift in the role of finance, where AI moves the CFO from scorekeeper to strategic intelligence hub, from reporting what happened to helping shape what happens next.

These also happen to be the areas where most PE-backed finance functions have done the least amount of work. The activities AI most meaningfully enables are exactly the ones finance teams have never had bandwidth to prioritize. The firms that recognize this first will have a 12-to-18-month head start on the exit narrative buyers are beginning to ask for.

There’s also a talent gap, but the leading companies are already closing it

Operating Partners rated the shortage of AI-literate finance talent at
4.1 out of 5 as a constraint on portfolio AI ambitions. This is the constraint that requires the longest runway to close, which is exactly why the firms taking it seriously now are making a smarter bet than the ones treating it as a future-state problem.

AI-literate finance leadership (people who can govern AI tools, make judgment calls on autonomous outputs, and translate machine-generated insight into board-ready narrative) cannot simply be hired. The market for this talent is thin, and more importantly, the capability is contextual: it requires understanding both the AI systems and the specific business they’re operating in. That combination is built through doing, not recruiting. The firms developing it now, inside their existing teams and equally important, through the right external partners, are compounding an advantage that a late hire cannot replicate on a short timeline. By the time a competitor realizes they need this capability, the firms that built it early will have 12 to 18 months of institutional knowledge that no job posting closes.

Tool-by-tool isn’t a playbook

The build-versus-buy-versus-activate question has largely resolved in PE-backed companies, but with a recognizable and somewhat concerning pattern.

Nearly half of Operating Partners describe a combination approach: tool-by-tool, use-case-by-use-case, no unified architecture, no shared measurement. It’s the path of least resistance in-hold, and the hardest thing to explain in a diligence call. Buyers who have seen one or two sophisticated AI finance builds know the difference between a curated stack and an accumulation, and they’re starting to ask which one they’re looking at.

83% have at least one agentic use case live

Covenant monitoring and budget variance escalation lead the deployment chart, and the logic is straightforward. These are high stakes, rules-based workflows where the performance standard is clear and the case for human-in-the-loop design is easy to make. An agent monitors continuously, surfaces exceptions with context, and routes them to a human for final judgment. That architecture, (broad AI coverage paired with targeted human oversight), is exactly the supervised autonomy model that most Operating Partners describe as the appropriate near-term boundary for agentic finance.

Board package drafting and close orchestration are close behind, and their presence on this list is significant because they are not simple automation use cases. They require AI to synthesize data across multiple systems, apply judgment about what is material, and produce output that is ready for senior review. The fact that 44% of Operating Partners report board package generation in active deployment suggests something important about how quickly the definition of what belongs in a finance team’s job description is shifting.

Supervised autonomy is the goal

How much autonomous action should AI be permitted to take in a 
PE-backed finance function? The Operating Partner consensus is supervised autonomy: AI executes within defined parameters, surfaces exceptions for human review, and maintains full audit trails of every action taken. That is a defensible posture in an industry where covenant compliance, board reporting, and LP communications carry real fiduciary weight.

The governance architecture for supervised autonomy is something CFOs must tackle now, as it requires someone who understands both the AI systems and the business context they operate in.

If multiples are the test, AI is starting to pass

The conversation about AI in PE-backed finance functions must end in the same place every other conversation about portfolio company investment ends: the multiple. Does AI create value? Does that value show up at exit? Are buyers pricing it?

Operating Partners in this survey have consistent views on all three questions. And the direction of those views is one of the more important signals in the data for sponsors thinking about where to focus portfolio AI investment in the next 12 to 18 months.

There’s strong conviction that AI-enabled finance infrastructure drives exit multiple expansion.

These are practitioners who have sat in recent exit processes, who have watched buyers conduct diligence, and who are making real capital allocation decisions based on their beliefs about where value will accrete. Their near-unanimous conviction that AI finance infrastructure matters at exit is, by itself, a meaningful market signal worth taking seriously.

The measurement ceiling is EBITDA

Most firms are measuring AI. The question is whether they’re measuring the right things, and whether the right things are even there to measure.

FTE impact/time savings is the most common measurement, selected by 67% of Operating Partners. Those metrics are real and useful. But they capture the floor of AI value: efficiency gains that reduce cost without necessarily changing what the business can do or what a buyer will pay for it. Only 38% are measuring revenue or margin impact. Only 29% include AI impact in board and LP reporting.

The disconnect between operational AI metrics and real value-creation metrics has three distinct explanations, and each one calls for a different fix.

The data reflects exactly that distribution. Firms at the top of this list are capturing real efficiency value, but are stopping short of the outcomes that move multiples:

The 100-day plan is getting shorter

The 100-day value creation plan is PE’s most foundational operating instrument. It signals what a sponsor believes, what management commits to, and what a buyer will inherit. AI is beginning to reshape what is achievable within that window in ways that early movers are treating as a genuine structural advantage. 19% of Operating Partners report significant or fundamental compression of finance transformation timelines, with initiatives that once required 12–18 months now achievable in six or fewer.

For buy-and-build strategies, that compression carries particular weight. When AI can standardize chart of accounts, automate intercompany eliminations, and accelerate financial integration across an add-on, digestion timelines compress materially. Faster synergy capture, cleaner audit trails, and earlier exit readiness follow. In a buy-and-build context, AI-native finance infrastructure is a deal thesis enabler that should be part of how acquisition targets are evaluated and prioritized at entry.

Buyers are already thinking about AI premiums

The market signal is getting clearer, even if valuation models have not fully caught up.

9% of Operating Partners have seen a demonstrable premium for AI-enabled finance capability in a completed transaction. 44% report that buyers are asking about it specifically in diligence, but the premium has not yet shown up in price. 33% expect it to materialize within two years.

Read together, these numbers describe a market in transition where the conviction is ahead of the pricing, which is exactly the window that PE exists to exploit. The firms building AI-native finance infrastructure now are positioning for a premium that is not yet fully priced in.

The best firms are redeploying savings

When AI compresses headcount needs in portfolio company finance functions, what happens to those savings matters as much as the savings themselves. The most common answer, selected by 38% of Operating Partners, is reinvestment into higher value strategic finance roles. The second most common is taking it straight to EBITDA.

The most expensive mistake: confusing faster processes with better ones

The final survey question asked Operating Partners to identify the most dangerous misconception that PE-backed CFOs and sponsors currently hold about AI in finance. The top response, selected by 41%, was that automation equals transformation. Automating existing processes (faster closes, automated reconciliations, AI-assisted reporting) is genuinely valuable. It reduces cost, improves accuracy, and frees up capacity.

What it does not do is change what finance is for. The firms that treat automation as the destination are building more efficient versions of the same finance function. The firms that treat it as the starting point are building something structurally different: a finance function that generates intelligence, a distinction that will show up in multiples.

The bottom-line: The firms scaling now will have options at exit

PE has always competed on the same edge: better information, faster decisions, more disciplined execution. AI amplifies that. Cleaner data means better decisions, made faster.

A CFO who owns AI as a mandate extracts more from every forecasting cycle and arrives at every diligence meeting with a more credible story. Over time, those advantages compound. Buyers are starting to see the difference between firms using AI to accelerate existing workflows and firms rebuilding finance around it.

What this survey documents is a market in genuine transition, where the distance between the leaders and the middle is widening faster than most portfolios realize. The 29% of Operating Partners who describe their firms as Systematizing or Leading are building finance functions that will forecast more credibly, operate more intelligently, and stand up more convincingly in diligence.

And that gap will not stay operational for long. It will become valuation.

You’ve seen what the market looks like. Take this quiz to see where you are or compare yourself on the tables below

The distance between the bottom and top of these tables is a measurement gap, a mandate gap, and in most cases a data infrastructure gap that has to be addressed before anything else compounds.